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Body Pose Prediction Based on Motion Sensor Data and Recurrent Neural Network

机译:基于运动传感器数据和经常性神经网络的身体姿态预测

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摘要

Mixed reality environments give better chances to provide constant help to the people in need. Applied there artificial intelligence models will provide ad hoc monitoring measures, which may be the best chance to protect life in dangerous conditions. In this article, we present our research on mixed reality system developed to detect symptoms of unusual poses at work, home, or other environments. Recurrent neural network is using sensor readings to evaluate the situation by the minimum necessary number of body sensors working as safe indicators. Research results show that the developed system is working with very high accuracy of 99.89% using just two body sensors working in a separate mode. The system can work without any special infrastructure or development in various environments to help workers and elder people in dangerous situations.
机译:混合现实环境使得更好的机会为需要的人提供不断的帮助。应用人工智能模式将提供临时监测措施,这可能是保护危险条件下生活的最佳机会。在本文中,我们展示了我们对开发的混合现实系统的研究,以检测工作,家庭或其他环境不寻常的姿势症状。经常性神经网络正在使用传感器读数来评估作为安全指标的最小必要数量的身体传感器的情况。研究结果表明,由于在单独模式下工作的两个体传感器,开发系统使用的高精度为99.89%。该系统可以在没有任何特殊的基础设施或各种环境中的开发,帮助工人和长老的人在危险的情况下。

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